Jaewon Hur1, Shengpu Tang1, Vidhya Gunaseelan2, Joceline Vu3, Chad M Brummett2, Michael Englesbe4, Jennifer Waljee5, Jenna Wiens6. 1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. 2. Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA. 3. Department of Surgery, University of Michigan, Ann Arbor, MI, USA. 4. Department of Surgery, University of Michigan, Ann Arbor, MI, USA; Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA. 5. Department of Surgery, University of Michigan, Ann Arbor, MI, USA; Michigan Opioid Prescribing Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA. Electronic address: filip@umich.edu. 6. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. Electronic address: wiensj@umich.edu.
Abstract
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients. METHODS: A retrospective study was conducted on 112,898 opioid-naïve adult postoperative patients from Optum's de-identified Clinformatics® Data Mart database. Potential predictors included sociodemographic data, comorbidities, and prescriptions within one year prior to surgery. RESULTS: Compared to linear models, non-linear models led to modest improvements in predicting refills - area under the receiver operating characteristics curve (AUROC) 0.68 vs. 0.67 (p < 0.05) - and performed identically in predicting new persistent use - AUROC = 0.66. Undergoing major surgery, opioid prescriptions within 30 days prior to surgery, and abdominal pain were useful in predicting refills; back/joint/head pain were the most important features in predicting new persistent use. CONCLUSIONS: Preoperative patient attributes from insurance claims could potentially be useful in guiding prescription practices for opioid-naïve patients.
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients. METHODS: A retrospective study was conducted on 112,898 opioid-naïve adult postoperative patients from Optum's de-identified Clinformatics® Data Mart database. Potential predictors included sociodemographic data, comorbidities, and prescriptions within one year prior to surgery. RESULTS: Compared to linear models, non-linear models led to modest improvements in predicting refills - area under the receiver operating characteristics curve (AUROC) 0.68 vs. 0.67 (p < 0.05) - and performed identically in predicting new persistent use - AUROC = 0.66. Undergoing major surgery, opioid prescriptions within 30 days prior to surgery, and abdominal pain were useful in predicting refills; back/joint/head pain were the most important features in predicting new persistent use. CONCLUSIONS: Preoperative patient attributes from insurance claims could potentially be useful in guiding prescription practices for opioid-naïve patients.
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